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Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

by 1,2, 1, 1, 3, 1,2, 1,2 and 1,2,*
1
School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
2
Hebei Engineering Laboratory of Wind Power/Photovoltaic Coupling Hydrogen Production and Comprehensive Utilization, Shijiazhuang 050018, China
3
Hebei Construction & Investment Group New Energy Company Ltd., Shijiazhuang 050051, China
*
Author to whom correspondence should be addressed.
Academic Editor: Detlef Schulz
Sustainability 2021, 13(18), 10453; https://doi.org/10.3390/su131810453
Received: 27 July 2021 / Revised: 14 September 2021 / Accepted: 16 September 2021 / Published: 20 September 2021
Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively. View Full-Text
Keywords: ultra-short-term wind power forecasting; deep extreme learning machine; sparrow search algorithm ultra-short-term wind power forecasting; deep extreme learning machine; sparrow search algorithm
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MDPI and ACS Style

An, G.; Jiang, Z.; Chen, L.; Cao, X.; Li, Z.; Zhao, Y.; Sun, H. Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine. Sustainability 2021, 13, 10453. https://doi.org/10.3390/su131810453

AMA Style

An G, Jiang Z, Chen L, Cao X, Li Z, Zhao Y, Sun H. Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine. Sustainability. 2021; 13(18):10453. https://doi.org/10.3390/su131810453

Chicago/Turabian Style

An, Guoqing, Ziyao Jiang, Libo Chen, Xin Cao, Zheng Li, Yuyang Zhao, and Hexu Sun. 2021. "Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine" Sustainability 13, no. 18: 10453. https://doi.org/10.3390/su131810453

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